Digital Commons Network^™

Exploring Big Data Management Approaches And Applications: A Case Study Of Real-Time Data Analytics In Air Traffic Management, Adeel Hashmi, Nouman Amjad, Muhammad Moiz Ullah Satti, Umar Hayat, Anam Mumtaz

Journal of Informatics and Web Engineering

The rapid proliferation of digital devices has generated vast amounts of data, presenting significant challenges in collection, processing, and analysis that traditional systems struggle to overcome. This study investigates big data management approaches, explicitly focusing on technologies capable of efficiently handling real-time data at scale. Within the context of Air Operations, we propose a Hadoop-based architecture designed to support the Observe-Orient-Decide-Act (OODA) loop and enhance air traffic management. By leveraging a distributed system deployed on a cloud-based platform, we demonstrate a cost-effective solution for optimised data processing and improved decision-making capabilities. Our analysis highlights the advantages of using Hadoop's distributed …

Enhancing Multi-Step Stock Price Forecasting With Social Media Sentiment And Engagement Metrics, Damilare Olaniyan

Electronic Theses and Dissertations

This thesis investigates whether social media sentiment can improve the accuracy of stock price prediction beyond traditional historical data. While financial markets have long relied on structured numerical indicators, the growing influence of public discourse on platforms like Twitter has introduced new opportunities for extracting market-relevant signals from unstructured text. The study focuses on four major technology firms and combines sentiment features derived from Twitter with historical stock prices in a hybrid machine learning framework. Engagement-weighted sentiment, linguistic complexity, and polarity intensity were extracted using natural language processing techniques and incorporated into classification and regression models. Results show that including …

Object-Based Image Analysis And Artificial Intelligence Identification Of Anthropogenic Disturbance On Lesser Prairie Chicken Habitat In Cheyenne County, Colorado, Tara Hoelzer

Geography and the Environment: Graduate Student Capstones

Renewable energy projects often require extensive landcover for their operations. When one of these projects encroaches into territory of threatened species, such as Lesser Prairie Chickens, an analysis of habitat suitability and human disturbance is required to proceed. Traditionally, this involved manually reviewing aerial imagery within a 6-mile radius, digitizing features, and interpreting them using a human technician—an approach that was time-consuming and prone to human error. By using pretrained AI models within Model Builder™, the identification of roads and structures was automated, making the process faster and more consistent than manual visual analysis. As AI and technology continue to …

Degraded Document Binarisation, Miriyala Ajith

Master’s Dissertations

In this study, I explored degraded document binarization by reviewing two recent model frameworks and implementing their models using PyTorch. The first model is based on cGANs, specifically the DE-GAN [41] framework, which enhances degraded documents by restoring their quality prior to binarization. The second model employs vision transformers [40], inspired by the DocBinFormer architecture, which uses an autoencoder in both the encoder and decoder for effective binarization. Both models were evaluated on the ISI-Bengali dataset. Experimental results demonstrate that DE-GAN improved document quality by 4% compared to the degraded input, while the vision transformer model achieved a 14% improvement, …

A New Model For Educational Program Assessments Using Automated Collective Concept Maps, Andrew R. Paullin

Master's Theses (2009 -)

This paper presents: Epistemological principles; the origins and theoretical foundations of concept maps; the current state of related works; the contributions, methods, results, and future road map of this research; and A New Model for Educational Program Assessments. An understanding that knowledge results from an evolution of cognitive structures enables a scientific approach to enhance the efficiency of learning. Building on prior works, this research provides a novel algorithm and tool to automatically create Collective Concept Maps from Individual Concept Maps by utilizing a dictionary, thesaurus, Natural Language Processing, Machine Learning / Artificial Intelligence, and domain expertise. Abstraction Filters enable …

A Survey On Immersive Cyber Situational Awareness Systems, Hussain Ahmad, Faheem Ullah, Rehan Jafri

All Works

Cyber situational awareness systems are increasingly used for creating cyber common operating pictures for cybersecurity analysis and education. However, these systems face data occlusion and convolution issues due to the burgeoning complexity, dimensionality, and heterogeneity of cybersecurity data, which damages cyber situational awareness of end-users. Moreover, conventional forms of human–computer interactions, such as mouse and keyboard, increase the mental effort and cognitive load of cybersecurity practitioners when analyzing cyber situations of large-scale infrastructures. Therefore, immersive technologies, such as virtual reality, augmented reality, and mixed reality, are employed in the cybersecurity realm to create intuitive, engaging, and interactive cyber common operating …

Automated Grain-Matrix Segmentation In Photomicrographs Of Clastic Sedimentary Rocks Using Neuro-Visual Algorithms, Rajdeep Das

Doctoral Theses

Sediments and sedimentary rocks, covering almost sixty-five percent of exposed earth’s surface, contain features that allow us to interpret ancient depositional environments, and their role in the evolution of life on earth. Study of sedimentary rocks is also relevant to exploration of geo-economic resources including petroleum, ore minerals, and groundwater as well as research in environmental geology, anthropological studies, and the bi-directional relation of man and environment. One of the important and crucial steps in this study of the detrital sedimentary rocks like sandstone, is to know the size and shape of the grains/clasts (fragments of different minerals or rocks), …

Virtual Assistant For Pest Management, Viswanada Chakravarthy Karri

Master’s Dissertations

Effective pest identification and management are essential for ensuring agricultural productivity, especially in regions with limited expert access. This work proposes a virtual assistant based on a Retrieval-Augmented Generation (RAG) [1] framework to support pest management tasks. The system utilizes a multimodal dataset consisting of pest images and annotated textual interactions, adapted from the AgriLLaVA corpus [2]. The assistant combines retrieval mechanisms with generative language models to generate contextually grounded responses. It is designed for deployment on local hardware with limited computational resources, integrating open-source models for both retrieval and generation. Preliminary results suggest that this approach can provide accurate, …

2.5d Dual-Encoder U-Net For Lesion Segmentation In Chest Ct Scans, Jagannath Mukkara

Master’s Dissertations

Accurate segmentation of lesions in chest CT scans plays a vital role in diagnosing and monitoring pulmonary diseases such as COVID-19. In this, we introduce a novel 2.5D[1] dual-encoder U-Net model[2] that utilizes both the central slice and its neighboring slices to improve segmentation accuracy while keeping computational demands manageable. Our model incorporates residual connections[3] and feature fusion[4] to effectively merge multi-slice contextual information, overcoming the limitations found in traditional 2D and 3D methods. To ensure a reliable evaluation and avoid data leakage, we used patient-level data splitting. We validate our approach on a carefully curated chest CT dataset, showing …

Morphology Based Galaxy Classification, Ayan Mukherjee

Master’s Dissertations

Galaxy evolution is an area of vital importance in current research as it is believed to hold vital clues of the past as well as the future of the universe. The structure or morphology of a galaxy acts as an indicator of its stage of evolution and may also shed light on the course of its future evolution. The deployment of high-resolution telescopes like James Webb Telescope has made available large amount of high-resolution images, thereby, facilitating the deployment of high-performance Machine Learning and Deep Learning techniques. In the proposed work, two different methods have been assessed to achieve better …

Notes On The Invariance Of Tautness Under Lie Sphere Transformations, Thomas E. Cecil

Mathematics and Computer Science Department Faculty Scholarship

An embedding ϕ : V → Sⁿ of a compact, connected manifold V into the unit sphere Sⁿ ⊂ Rⁿ⁺¹ is said to be taut, if every nondegenerate spherical distance function d_p, p ∈ Sⁿ, is a perfect Morse function on V , i.e., it has the minimum number of critical points on V required by the Morse inequalities. In these notes, we give an exposition of the proof of the invariance of tautness under Lie sphere transformations due to ´Alvarez Paiva. First we extend the definition of tautness of submanifolds of S …

Virtual Reality’S Impact On Tourist Attitudes In Islamic Religious Tourism: Exploring Emotional Attachment And Vr Presence, Eman Alkhalifah, Ramy Hammady, Mahmoud Abdelrahman, Alyaa Darwish, Ella Cranmer, Ons Al-Shamaileh, Aikaterini Bourazeri, Timothy Jung

All Works

This study explores the integration of immersive technologies, specifically virtual reality (VR), to enhance tourist experiences in the rapidly expanding religious tourism sector. Despite VR’s potential, limited research has examined its impact on religious tourism. This study addresses this gap by investigating the role of emotional attachment in influencing VR presence during pre-, on-site, and postexperiences of VR-mediated religious tourism. A quantitative survey was conducted with 201 respondents who participated in VR religious tourism activities. The empirical analysis, conducted using SPSS and AMOS structural equation modeling (SEM), assessed how VR-mediated religious tourism impacts VR presence and tourist attitudes before actual …

Neat Vs. Scruffy: How Early Ai Researchers Classified Epistemic Cultures Of Knowledge Representation, Lindsay Poirier

Statistical and Data Sciences: Faculty Publications

Accounts of the history of symbolic artificial intelligence (AI) often categorize researchers and their programs through binary oppositions: logicist versus procedural, or alternatively, neat versus scruffy. These terms were often referenced throughout the 1970s and 1980s by AI researchers at institutions in the United States and Europe to document epistemic and esthetic differences in approaches to knowledge representation. In this article, I historicize and situate these binary oppositions, showing how researchers leveraged them in discourse to demarcate the beliefs and commitments underpinning certain AI approaches. I argue that the labels helped knowledge representation scholars identify, trouble, and grapple with the …

Forecasting Influenza Rates Using Machine Learning: A Study Of Chatgpt's Predictive Accuracy, Sara Saleh

University Honors Theses

This study evaluates ChatGPT's ability to forecast influenza rates, such as the number of flu cases, hospitalizations, and death during peak season periods using CDC data, and comparing forecasts against actual results to calculate statistical accuracy and consistency. Influenza forecasting is essential for public health planning, but traditional methods may not always provide timely or accurate predictions. In this research study, ChatGPT was utilized to predict the influenza rates for the following week based on the previous week's data obtained from the FluView surveillance system. The predicted rates were compared to the actual influenza rates to assess the model's overall …

Tellings Of The Pacific Ocean: A Landscape-Based Approach For Multispecies Design And Hci, Maliheh Ghajargar

Engineering Faculty Articles and Research

Environmental disturbances induced by climate change have caused significant changes in our ecosystems and are threatening the health of our environments. As a response to this issue, a growing body of work has emerged in HCI and design, which seeks to foreground more-than-human stories in support of making more sustainable and just futures. This research contributes to this broad agenda by probing graphic novels as a multispecies storytelling method for design and HCI. Combining ideas from Anna Tsing’s adventures of landscape and from HCI and design’s use of sequential art (e.g., storyboards), we use landscape as the main protagonist of …

A Framework For Scalable And Controlled Hallucination Data Collection, Lin Ting Liang

Computer Science Senior Theses

This thesis addresses a key bottleneck in hallucination research: the scarcity and limitations of hallucination benchmark datasets. Existing datasets typically focus on a single type of hallucination and are expensive to produce due to the need for manual prompt creation and annotation. To overcome these challenges, we propose a novel mixture-of-experts (MoE) adversarial framework that actively induces hallucinations. Our framework employs three large language model (LLM) agents that iteratively and adversarially revise prompts to provoke hallucinated responses from a target question-answering model. It automates the generation of both intrinsic hallucinations (logical inconsistencies) and extrinsic hallucinations (inclusion of unverifiable external information). …

A First Look Into Parental Strategies, And Challenges Around Children’S Device Usage In Urban Nepal, Rizu Paudel, Prakriti Dumaru, Ankit Shrestha, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

There have been substantial changes in the landscape of technology use by children in Global South during COVID-19, when the shift to online learning platforms necessitated parents to avail personal devices (e.g., smartphones, computers) for their children to fulfill their educational needs. However, the use of devices by children are not limited to serving educational purpose only. Our study positions itself in a critical post-pandemic period in Nepal, characterized by the increase in device use by children, while a little study to date, investigated parental mediation in this developing country. To this end, we conducted semi-structured interviews with 20 parents, …

Motionteller: Multi-Modal Integration Of Wearable Time-Series With Llms For Health And Behavioral Understanding, Aiwei Zhang, Arvind Pillai, Andrew Campbell, Nicholas C. Jacobson

Computer Science Senior Theses

As wearable sensing becomes increasingly pervasive, a key challenge remains: how can we generate natural language summaries from raw physiological signals such as actigraphy - minute-level movement data collected via accelerometers? In this work, we introduce MotionTeller, a generative framework that natively integrates minute-level wearable activity data with large language models (LLMs). MotionTeller combines a pretrained actigraphy encoder with a lightweight projection module that maps behavioral embeddings into the token space of a frozen decoder-only LLM, enabling free-text, autoregressive generation of daily behavioral summaries.

We construct a novel dataset of 54,383 ⟨actigraphy, text⟩ pairs derived from real-world NHANES recordings, and …

An Efficient Algorithm For Finding High Harmonic Centrality Vertices In Graphs, John Balson

Computer Science Senior Theses

In this thesis we consider the problem of harmonic centrality in graphs. This measure is used widely in the study of real-world complex networks. In particular, we consider the problem of finding a high harmonic centrality vertex in time much faster than $O(mn)$, the time required to calculate the exact harmonic centrality of all vertices in a graph. The problem of calculating centrality measures faster has received much attention in recent years, since calculating the exact harmonic centrality of all vertices can be infeasible for large graphs; hence, faster algorithms are needed. This thesis proposes a new algorithm for finding …

An Approach To Stylometry Using Causal Language Models, Harrison F. Stropkay

Computer Science Senior Theses

We present a novel stylometric approach using large language models. By training separate models on individual authors' works, we find that each model achieves lower cross-entropy loss when predicting text from its training author compared to other authors' texts. Moreover, for any given text, the model trained on its true author’s corpus yields the lowest loss. We suggest that, in this way, a model trained on one author's works embodies the unique writing style of that author. We demonstrate our approach on works by eight known authors. This approach also confirms that R. P. Thompson wrote the well-studied 15th book …

Enhancing Iot Decentralization With Iota 2.0: A Dag-Based Fast Probabilistic Consensus Framework, Ayat N. Kadhum, Ahmed M. Al-Salih

Journal of Intelligent Informatics, Networking, and Cybersecurity

The integration of IoT and blockchain enhances security, trust, and data integrity but is hindered by security attacks, scalability, and high latency. In this work, a more efficient method of consensus using Directed Acyclic Graph (DAG)-based Fast Probabilistic Consensus (FPC) and Edwards-Curve Digital Signature Algorithm (EdDSA) is proposed to yield better security, efficiency, and decentralization. By eliminating mining, resource use is optimized, and consensus is hastened. Experimental results show a high throughput of 7228.05 Transactions Per Second (TPS), rapid consensus formation in just 7.62 rounds on average, and high adversary resilience, with the system successfully mitigating 89% of adversarial attacks. …

Cross-Modality Learning For Predicting Ihc Biomarkers From H&E-Stained Whole-Slide Images, Amit Das

Computer Science Senior Theses

Hematoxylin and Eosin (H&E) staining is a cornerstone of pathological analysis, offering reliable visualization of cellular morphology and tissue architecture for cancer diagnosis, subtyping, and grading. Immunohistochemistry (IHC) staining, an important ancillary study, provides molecular insights by detecting specific proteins within tissues, enhancing diagnostic accuracy, and improving treatment planning. However, IHC staining is costly, time-consuming, and resource-intensive, requiring specialized expertise. To address these limitations, this study proposes HistoStainAlign, a novel deep learning framework that predicts IHC staining patterns directly from H&E whole-slide images (WSIs) by learning joint representations of morphological and molecular features. The framework integrates paired H&E and IHC …

Biomarker-Guided Imaging And Ai-Augmented Diagnosis Of Degenerative Joint Disease, Rahul Kumar, Kyle Sporn, Aryan Borole, Akshay Khanna, Chirag Gowda, Phani Paladugu, Alex Ngo, Ram Jagadeesan, Nasif Zaman, Alireza Tavakkoli

Department of Medicine Faculty Papers

Degenerative joint disease remains a leading cause of global disability, with early diagnosis posing a significant clinical challenge due to its gradual onset and symptom overlap with other musculoskeletal disorders. This review focuses on emerging diagnostic strategies by synthesizing evidence specifically from studies that integrate biochemical biomarkers, advanced imaging techniques, and machine learning models relevant to osteoarthritis. We evaluate the diagnostic utility of cartilage degradation markers (e.g., CTX-II, COMP), inflammatory cytokines (e.g., IL-1β, TNF-α), and synovial fluid microRNA profiles, and how they correlate with quantitative imaging readouts from T2-mapping MRI, ultrasound elastography, and dual-energy CT. Furthermore, we highlight recent developments …

Level Of Service Criteria For Urban Arterials With Heterogeneous And Undisciplined Traffic Streams, Afzal Ahmed, Farah Khan, Syed Faraz Abbas Rizvi, Fatma Outay, Muhammad Faiq Ahmed, Muhammad Adnan

All Works

Accurate evaluation of the prevailing traffic operations plays an important part in developing sustainable transport systems. This research examines the suitability of the level of service (LOS) criteria developed by the Indian and United States (US) Highway Capacity Manuals (HCM) for heterogeneous and undisciplined traffic streams and proposes new criteria using a data-driven approach. Traffic data were collected from a selected major arterial in Karachi, and fundamental diagrams were developed using these data. These fundamental diagrams and field-collected data were analyzed using the K-mean clustering approach to examine the actual traffic states at various LOS bands used in practice. Associating …

A Survey Of Feature Attribution Techniques In Explainable Ai: Taxonomy, Analysis And Comparison, Mohd Amril Nazir, Edmund Evangelista, Syed Muhammad Salman Bukhari, Ravishankar Sharma

All Works

The feature attribution methods have become central to explainable artificial intelligence (XAI), providing critical insights into how machine learning (ML) models make individual and aggregate decisions. This survey presents a complete taxonomy of feature attribution techniques, organizing them into model-agnostic and model-specific categories while highlighting extensions such as rule-based and attention-based explanations. We analyze formal definitions of each method, mathematical formulations, application contexts, strengths, and limitations. A comparative analysis highlights key trade-offs among model flexibility, computational cost, explanation fidelity, and interpretability. In addition to theoretical perspectives, we provide practical comparisons of selected methods on benchmark tasks to guide real-world applicability. …

Deep Imputation Of Missing Values Using Feature And Sample Attention, Ibna Kowsar

Tennessee State University Alumni Theses and Dissertations

The handling of missing values is a pervasive challenge in tabular data sets, particularly in electronic health records (EHR), where incomplete data can hinder predictive modeling. Data with missing values are unfit for machine learning, whereas the imputation of missing values affects data quality and data-driven outcomes. Traditional statistical and machine learning-based imputation techniques often struggle with high missing rates and complex missing patterns. This thesis investigates the deep learning of attention between features and between samples in missing value imputation. These two attention mechanisms jointly capture the row-column structure of tabular data. It presents a novel deep learning framework …

From Lists To Infinite Scroll: A Comparative Analysis Of Youtube’S Two Recommendation Algorithms, Selimhan Dagtas

Theses and Dissertations

As social media platforms increasingly dominate information consumption, the role of recommendation algorithms in determining user experience has grown both in complexity and impact. This thesis investigates the behavioral patterns and algorithmic preferences embedded within YouTube’s recommendation systems, comparing long- form videos with the rapidly growing category of short-form content, YouTube Shorts. Through a combination of automated data collection, engagement metric analysis, emotional sentiment detection, and toxicity assessment, this study analyzes the evolution of content over successive recommendation depths. Using a controlled digital environment, the research explores how content recommendations change in response to user behavior, including varying watch times …

"Opting Out Of Ai”: Exploring Perceptions, Reasons, And Concerns Behind Faculty Resistance To Generative Ai, Aya Shata

Hank Greenspun School of Journalism and Media Studies Faculty Research

Research on Generative Artificial Intelligence (GAI) in higher education primarily focuses on faculty use and experiences, with limited attention given to why some abstain from using it. Drawing from Innovation Resistance Theory, this study aims to address this gap by exploring the perceptions of both faculty users and non-users of GAI, identifying the reasons and concerns why they avoid GAI. A survey of 294 full-time higher education faculty from two mid-size U.S. public universities was conducted. Using qualitative and quantitative analysis, results show that over one-third of the faculty members opted out of using GAI for five primary reasons: not …

Video Game Hacking, A General Problem With Generalized Solutions, Luke Rowe

Master's Theses

As video games continue to get more popular and lucrative, the number of malicious actors seeking to exploit them grows with it. As this industry expands, so does the importance of securing games against cheating and abuse. This thesis aims to educate developers to help mitigate the abuse of video games by these malicious actors. The goal of this thesis is to provide a foundational framework for thinking like a hacker and how to make games harder to abuse once a hacker bypasses conventional anti-cheat software.

This thesis outlines some of the most common cheating methods and provides general context …

Enabling Automatic Solar Pv Array Identification Using Big Satellite Imagery, Qi Li, Keyang Yu, Carson Snow, Dong Chen

Computer Science Faculty Research and Publications

Recently, there has been a growing interest in automatically collecting distributed solar photovoltaic (PV) installation information in smart grid systems, including the quantity and locations of solar PV deployments, as well as their profiling information across a given geospatial region. Most recent approaches are still suffering low detection accuracy due to insufficient sample and principal feature learning when building their models and also separation of rooftop object segmentation and identification during their detection processes. In addition, they cannot report accurate multi-deployment results. To address these problems, we design a new system-SolarDetector+, which can automatically and accurately detect and profile distributed …